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Found 10 papers, 5 papers with code
Marginalized Beam Search Algorithms for Hierarchical HMMs
Inferring a state sequence from a sequence of measurements is a fundamental problem in bioinformatics and natural language processing.
Optimized Precoding for MU-MIMO With Fronthaul Quantization
We also propose a heuristic low-complexity approach to quantized precoding.
Convex Quantization Preserves Logconcavity
A logconcave likelihood is as important to proper statistical inference as a convex cost function is important to variational optimization.
A matrix-inverse-free implementation of the MU-MIMO WMMSE beamforming algorithm
From a theoretical viewpoint, we establish its convergence to a stationary point of the WSR maximization problem.
Inertial Navigation Using an Inertial Sensor Array
We present a comprehensive framework for fusing measurements from multiple and generally placed accelerometers and gyroscopes to perform inertial navigation.
Reinforcement Learning for Efficient and Tuning-Free Link Adaptation
In this context, we propose a latent learning model for link adaptation that exploits the correlation between data transmission parameters.
Deep unfolding of the weighted MMSE beamforming algorithm
Motivated by the recent success of deep unfolding in the trade-off between complexity and performance, we propose the novel application of deep unfolding to the WMMSE algorithm for a MISO downlink channel.
Thompson Sampling for Linearly Constrained Bandits
We address multi-armed bandits (MAB) where the objective is to maximize the cumulative reward under a probabilistic linear constraint.
Constrained Thompson Sampling for Wireless Link Optimization
We model rate selection as a stochastic multi-armed bandit (MAB) problem, where a finite set of transmission rates are modeled as independent bandit arms.
SpotNet - Learned iterations for cell detection in image-based immunoassays
Accurate cell detection and counting in the image-based ELISpot and FluoroSpot immunoassays is a challenging task.
